Anomalities in mechanisms can sometimes be characterized by their accoustic signatures or events. The accoustic events are typically rare, and overwhelmed by background noise. Situations in which this background noise is cyclo-stationary are common, e.g., with rotating mechanisms, where the background noise signal is not periodic, but its statistical properties are periodic.
Detecting relatively rare and typically low-energy events in such background noise can lead to useful applications, but is challenging. One such application is that of vehicle engine knock detection. When an engine rotates at a high speed, and depending on running and environmental conditions, uncontrolled explosions can occur at certain cycles, which can lead to potential damage to the engine.
However, sometimes the most fuel efficient operating point corresponds to conditions in which knocks occur, and therefore engine manufacturers typically try to control their engine such that they run in conditions as close as possible to knocking conditions, but without creating significant knocks.
Knock detection sensors and devices are required to control the running conditions of engines. The sensors are typically accelerometers. The detection devices, e.g., engine control units, generally tend to rely on simple filtering and thresholding techniques, and their accuracy is limited. Those devices and sensors are typically tuned by an expert who detect knocks by listening to the engine and calibrate the sensors so that: their location as well as their detection thresholds are set such that the detections match those of the expert. The expert can use various sensors processed through some signal processing methods as side information, e.g., cylinder pressure, ion current, etc. Such calibration is highly time-consuming, error-prone, tiring, and requires a highly skilled expert. It would be useful to have a method of replacing or assisting an expert to automatically tune the control unit and sensor.